Business Environment in South Asia: Foreign-Owned Firms’ Perspectives
Bibliographic record
Abstract
This paper studies the foreign-owned firms’ (hereinafter, FOFs) perspectives about selected indicators of business environment in four South Asian countries - Bangladesh, India, Pakistan and Sri Lanka. For the last few decades, these countries have eagerly sought to increase the inflow of foreign capital. Using the World Bank’s Enterprise Survey 2017 data, this study identifies political instability, poor infrastructure, and pervasive corruption as the three biggest obstacles that FOFs face in their business operations in these countries. The other obstacles include inadequately educated workforce, customs and trade regulations, crime, theft and disorder, tax administration, business licensing and permits, and access to finance. This study also finds that since 2007 the business environment for FOFs has improved remarkably in Bangladesh, improved slightly in India, improved modestly in Sri Lanka, but worsened noticeably in Pakistan. This study adds to our knowledge of factors that affect the dynamics of foreign capital inflow, which should be helpful in devising strategies to attract more foreign capital to developing countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".